Brain and Cognition 88 (2014) 26–34

Contents lists available at ScienceDirect

Brain and Cognition journal homepage: www.elsevier.com/locate/b&c

Association of television violence exposure with executive functioning and white matter volume in young adult males Tom A. Hummer a,b,⇑, William G. Kronenberger b, Yang Wang a, Caitlin C. Anderson c, Vincent P. Mathews a a

Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States c Department of Psychology, Iowa State University, Ames, IA, United States b

a r t i c l e

i n f o

Article history: Accepted 22 April 2014 Available online 16 May 2014 Keywords: Executive function Television Media violence Inhibition Voxel-based morphometry

a b s t r a c t Prior research has indicated that self-reported violent media exposure is associated with poorer performance on some neuropsychological tests in adolescents. This study aimed to examine the relationship of executive functioning to violent television viewing in healthy young adult males and examine how brain structure is associated with media exposure measures. Sixty-five healthy adult males (ages 18–29) with minimal video game experience estimated their television viewing habits over the past year and, during the subsequent week, recorded television viewing time and characteristics in a daily media diary. Participants then completed a battery of neuropsychological laboratory tests quantifying executive functions and underwent a magnetic resonance imaging (MRI) scan. Aggregate measures of executive functioning were not associated with measures of overall television viewing (any content type) during the past week or year. However, the amount of television viewing of violent content only, as indicated by both past-year and daily diary measures, was associated with poorer scores on an aggregate score of inhibition, interference control and attention, with no relationship to a composite working memory score. In addition, violent television exposure, as measured with daily media diaries, was associated with reduced frontoparietal white matter volume. Future longitudinal work is necessary to resolve whether individuals with poor executive function and slower white matter growth are more drawn to violent programming, or if extensive media violence exposure modifies cognitive control mechanisms mediated primarily via prefrontal cortex. Impaired inhibitory mechanisms may be related to reported increases in aggression with higher media violence exposure. Ó 2014 Elsevier Inc. All rights reserved.

1. Introduction Concern about the potential psychological and neural effects of exposure to violence depicted on broadcast television and other media has led to an enormous scientific literature, much of which has revealed an association between viewing violence on television and risk of subsequent aggressive thoughts, emotions, and behaviors (Anderson et al., 2003; Paik & Comstock, 1994). Longitudinal research has indicated that childhood television violence exposure predicts adult aggression, even when controlling for such factors as intelligence, family environment and, notably, the amount of aggression the child originally demonstrated (Eron, Huesmann, Lefkowitz, & Walder, 1972; Huesmann, Moise-Titus, Podolski, & Eron, 2003; Lefkowitz, Eron, Walder, & Huesmann, 1977). ⇑ Corresponding author. Address: 705 Riley Hospital Drive, Riley Outpatient Center, Room 4300, Indianapolis, IN 46202, United States. Fax: +1 317 948 0609. E-mail address: [email protected] (T.A. Hummer). http://dx.doi.org/10.1016/j.bandc.2014.04.010 0278-2626/Ó 2014 Elsevier Inc. All rights reserved.

A number of explanations for this relationship between media violence and aggression have been offered, ranging from imitation and observational learning to more complex influences on worldview and the nature of social interactions. The General Aggression Model (GAM) encompasses these ideas, proposing that repeated exposure to media violence causes changes in an individual’s aggressive beliefs, social–cognitive schemata, behavioral scripts, and desensitization to violence (Bushman & Anderson, 2002), all of which contribute to a more aggressive personality. In addition, extensive observation of violent scenes can reduce natural inhibitions toward conflict and aggression, which, combined with altered social perceptions, may increase violent behavior (Bushman & Geen, 1990). When influenced by environmental variables and/or physiological arousal, it is more difficult for individuals to control aggressive thoughts and to inhibit themselves from engaging in injurious words or actions when inhibitory mechanisms are compromised. Such cognitive control and inhibition are prominent facets of executive functioning, underlining the importance of

T.A. Hummer et al. / Brain and Cognition 88 (2014) 26–34

neuropsychological factors in the relationship between media violence exposure and aggressive behavior. Executive function encompasses a variety of higher-level cognitive processes that includes control of behavior, inhibition, planning, problem solving, working memory, and organization. Kronenberger et al. (2005a) reported that high media violence exposure was associated with poorer executive functioning in adolescents, as measured by both behavioral and questionnaire measures. Notably, this relationship remained strong when statistically controlling for total media exposure, as well as for demographic, diagnostic and intelligence measures. Other investigations have indicated a relationship between overall childhood television viewing and attention problems (Huesmann et al., 2003; Landhuis, Poulton, Welch, & Hancox, 2007), though the strength and timing of such effects have been called into question (Foster & Watkins, 2010; Zimmerman & Christakis, 2007). The neural mechanisms underlying this reported relationship between media violence exposure and executive dysfunction are unclear. Executive processes are associated primarily with frontal lobe functioning, which provides a neurobiological link with media violence exposure and aggression. Aggressive individuals demonstrate diminished activity and structural abnormalities in prefrontal cortex, particularly related to impulsive and disinhibited behaviors (Brower & Price, 2001; Giancola, 1995). In addition, exposure to violent media has been associated with reduced frontal lobe activity (Mathews et al., 2005), as has playing a violent video game (Hummer et al., 2010; Wang et al., 2009). Violent video game players have also demonstrated a reduced P300 response to violent images, which was associated with trait aggression (Bartholow, Bushman, & Sestir, 2006). Despite these connections, little research has examined the relationship between media violence exposure and structural brain development. In adolescent males, increased violent media exposure is associated with reduced lateral orbitofrontal cortex density (Strenziok et al., 2010), using a subscale from the Children’s Report of Exposure to Violence (Cooley, Turner, & Beidel, 1995). In addition, exposure to real-life violence is related to reduced visual cortex gray matter volume (Tomoda, Polcari, Anderson, & Teicher, 2012) or maturation of visual–limbic white matter tracts (Choi, Jeong, Polcari, Rohan, & Teicher, 2012). While this research is promising, much additional work is necessary to characterize the relationship between media violence exposure and brain development. The relationship between brain structure and executive function is somewhat better understood, however. In studies of healthy young adults, both gray and white matter volume have been found to be significantly correlated with neuropsychological performance, particularly in lateral prefrontal regions, using a variety of measures (Brickman et al., 2006; Gur et al., 1999; Matsuo et al., 2009; Newman, Trivedi, Bendlin, Ries, & Johnson, 2007). In these studies, better performance on executive function measures is associated with larger gray or white matter volume within networks involved in executive control (particularly attention and inhibitory processes). In addition, greater growth and development of frontoparietal white matter connections, such as the superior longitudinal fasciculus, is tied to better working memory, attention, delay discounting, and interference control (Burzynska et al., 2011; Mabbott, Noseworthy, Bouffet, Laughlin, & Rockel, 2006; Olson et al., 2009; Silveri, Tzilos, & Yurgelun-Todd, 2008; Tamnes et al., 2010a). Because media violence viewing has been tied to poorer executive functioning (Kronenberger et al., 2005a), we hypothesize that higher television violence exposure is related to reduced gray and white matter volume, particularly in lateral prefrontal regions or frontoparietal connections. This study aims to extend previous work showing a relationship between media violence exposure and neuropsychological function and to examine whether television violence is related to volumetric

27

measures of gray and white matter. Establishing this relationship is an important step into identifying potential long-term relationships of media exposure to brain development. To this end, a battery of neuropsychological tests were provided to young adult males who also underwent a structural magnetic resonance imaging (MRI) scan. Voxel-based morphometry (VBM) techniques were utilized to examine potential relationships of television violence exposure with gray or white matter volume in the young adult brain. This investigation extends previous media exposure research in several additional ways, including use of an adult sample, which likely has more mature and stable neuropsychological functioning than in adolescent years. In addition, the population in this investigation was more rigorously controlled, consisting solely of adult males with low levels of video game experience and no clinical diagnoses. The use of men who only sparingly play video games provides a more powerful look specifically at violent television exposure, by essentially controlling for video game exposure, which is another common source of media violence exposure for young adult males. Finally, multiple measures of media violence exposure were utilized in this study, including the use of daily media diaries, in order to more accurately assess television viewing habits. Consistent with prior data, we hypothesized that increased television violence exposure would be associated with poorer executive functions, particularly those related to inhibitory processes. We also hypothesized lower prefrontal gray matter volume and reduced frontoparietal white matter volume with higher levels of television violence exposure. In order to examine this idea, healthy male participants underwent neuropsychological testing and an MRI scan and also provided detailed measurements of television viewing time and content. 2. Methods 2.1. Participants Seventy-three participants aged 18–29 (inclusive), all with a nonverbal IQ greater than 80 (Kaufman & Kaufman, 2004), were recruited to take part in a larger project on the effects of media violence on neuropsychological and brain functioning. These young men were recruited via informational flyers and online notices at local hospital and university settings. Sixty-five participants completed testing, an MRI scan and at least six days of daily media diary reports (details below) and were included in data analyses. Institutional ethics approval was provided for the study protocol by the university Institutional Review Board, and written informed consent was obtained before any procedures were initiated. All participants were males free of self-reported psychiatric diagnoses, as indicated by scores on the Adult Self-Report Inventory (AI-4; Gadow, Sprafkin, & Weiss, 2004) and self-reported psychiatric history. In addition, participants were required to have a self-reported history of no more than 5 hours per week of video game play or 2 hours per week specifically of violent video game play in the past year. 2.2. Procedure During their first visit, participants completed questionnaires about demographic characteristics, potential psychiatric symptoms, behavior, and past media exposure. They were also instructed on the use of daily media diaries that they were to fill out at home during the subsequent week. Participants were instructed to refrain from video game play during this week as part of the larger study protocol, although these participants were already minimal game players. After 1 week, participants returned

28

T.A. Hummer et al. / Brain and Cognition 88 (2014) 26–34

for a second visit, during which they completed additional questionnaires and neuropsychological tests and underwent an MRI scan. 2.3. Measures 2.3.1. Media exposure indicators Two separate questionnaires were utilized to quantify television violence exposure: (1) A self-report of estimated past-year exposure; and (2) a daily take-home diary filled out over the course of a week. These measures required participants to estimate TV, video game, and movie theater exposure. 2.3.1.1. Media Exposure Measure (MEM). The MEM (Kronenberger et al., 2005a, 2005b) was utilized to quantify past exposure to violent and other types of media. Subjects reported their average weekly exposure to television (including films, such as DVDs, watched on TV, and internet videos/online TV viewing) over the past year (MEM-PY) at visit 1. The MEM-PY asks for an estimate of the total amount of television viewing in a typical week during the past year (from an anchored 0–9 scale; see Table 1 for possible responses) as well as the degree of violent content in the programming for various categories, including physical threats, fighting, injury, and use of weapons (from 0 – Not present/Very rare to 3 – Most of the time). 2.3.1.2. Daily Media Diary (DMD). Participants completed the DMD at home each evening during the week between visits. The DMD is a brief questionnaire about media exposure, based on the MEM, rescaled for daily use (Table 1) and able to measure violent content identically to the MEM-PY. As in the MEM-PY, participants circled a single answer for each question. Separate, identical sheets were provided for each day between visits. On the MEM-PY and DMD, the rating for injury, defined as ‘‘severe injury or death (may not be explicitly shown, but someone was hurt badly)’’ was used as the measure for television violence. 2.3.1.3. Video Game Rating Inventory (VRI). To quantify past-year video-game experience, participants were provided the VRI at visits one and two (Hummer et al., 2010; Wang et al., 2009). The VRI asked participants to estimate hours of video game play in an average week, and specifically hours of violet video game play in an average week. The averages of the two visits’ responses are reported and analyzed, though participants were excluded if their responses on either day exceeded screening requirements for either total or violent video game exposure.

Table 1 Response options for media exposure measure and daily media diaries. Response

MEM-PY

DMD

0 1 2 3 4 5 6 7 8 9

None at all Less than an hour per week About 1 hour per week About 2 hours per week About 4 hours per week About 7 hours per week About 14 hours per week About 21 hours per week About 28 hours per week More than 35 hours per week

None at all Less than 10 minutes About 10 minutes About 15 minutes About 30 minutes About 1 hour About 2 hours About 3 hours About 4 hours About 5 hours or more

Questionnaire response options for the Past Year version of the Media Exposure Measure (MEM-PY) and daily media diaries (DMD). Participants were asked to circle one number to reflect viewing during a typical week over the Past Year (MEM-PY) or the designated day (DMD).

2.3.2. Neurocognitive measures 2.3.2.1. Stroop Color–Word Test (SCWT). The classic Stroop test (Golden, 1978; Stroop, 1935) measures inhibition and interference control based on how well participants can control automatic responses (word reading) to properly perform a more effortful competing task (color identification). The utilized version of the SCWT consisted of three separate timed subtests, performed consecutively: word reading, in which participants read a list with RED, GREEN, and BLUE randomly repeated (in black ink); color naming, which required ink color identification of a series of strings of X’s (XXXX) that were red, green, or blue in color; and the color–word test, in which participants identified the ink color of words that spell out a different color (e.g. ‘‘RED’’ in blue ink). The Stroop color–word score was the number of color words correctly identified by subjects in 45 s on the latter subtest (color– word). In addition, an interference score was determined based on how much the color–word score differed from what would be predicted from word-reading and color-naming subtest performance, using published standards (Golden, 1978; Hummer et al., 2011). A higher interference score indicates better-than-expected performance, signifying better executive functioning. 2.3.2.2. Counting Interference Test (CIT). A numerical analog to the SCWT is the CIT (Hummer et al., 2011; Mathews et al., 2005), which requires individuals to quickly read digits (1, 2, or 3); count a series of X’s (X, XX, or XXX); or count the number of digits (e.g., 3; 11; 222) for each item in a series during subsequent 45-s time periods. This test likewise measures inhibitory processes, and the primary score is the number of responses during the latter subtest (number-count). Because a standard interference measure has not been derived for the CIT, an interference score was defined as the residuals from a linear regression with number-count score as the dependent variable and digit-naming and X-counting scores as independent (explanatory) variables. 2.3.2.3. Digit span. Subjects performed digit span tests in which the experimenter read a series of numbers, which the participant then repeated back in either a forwards or backwards manner (Cohen, 1997), depending on the test. The number of digits in the series increased until two consecutive series were incorrectly repeated. The score is the number of correctly recalled numerical series. The digit span tests measure verbal working memory capabilities and auditory attention. 2.3.2.4. Conners’ Continuous Performance Test (CCPT). The CCPT (Conners, 2000) is a measure of attention and inhibition in which participants are shown a series of letters, one at a time, via a computer. For each letter except X, a button is to be pressed. If X is shown (approximately 10% of trials), the individual must withhold from pressing the button. Failures to inhibit a button-press for the letter X (commission errors) signify impulsive responding. Variability in the reaction time to press the button in response to a non-X stimulus (reaction-time standard error, RTSE) quantifies attentional drift (inconsistency) over the course of the test, which lasts for approximately 14 minutes. 2.3.2.5. Kaufman brief intelligence test – Second edition (K-BIT2). The Matrices subtest of the K-BIT-2 (Kaufman & Kaufman, 2004) was used to evaluate nonverbal intelligence at visit 1. 2.3.3. Neuroimaging MRI scans took place on a 3T Siemens TIM Trio scanner with an 12-channel head coil (Siemens, Erlangen, Germany). A threedimensional T1-weighted magnetization-prepared rapid acquisition gradient echo (MP-RAGE) anatomical scan was performed, with 160 contiguous 1.2-mm thick sagittal slices acquired (voxel

29

T.A. Hummer et al. / Brain and Cognition 88 (2014) 26–34

size: 1  1  1.2 mm). Additional parameters included repetition time, 2300 ms, echo time, 2.91 ms; inversion time, 900 ms; flip angle 9°, acquisition matrix 240  256, field-of-view 256 mm. 2.4. Analysis For each participant, weekly estimates of total television viewing time were obtained from both the MEM-PY and DMD. For the DMD, this value was the mean daily value over the past week. Violent television exposure was calculated from the MEM-PY by multiplying total television viewing time by the injury rating score. In the case of the DMD, this score was the mean daily total television viewing multiplied by the mean daily injury value on days with any television watched. The relationship of media measures to executive functioning scores was tested using primary executive function outcome measures from each task: Stroop color–word, CIT number-count, digit span forward, digit span backward, CCPT commissions, and CCPT RTSE. Two measures are taken from the CCPT to reflect the two distinct components of this task: inhibiting responses when appropriate and maintaining extended attention. The Stroop and CCPT measures were included in our previous investigation of adolescents (Kronenberger et al., 2005a) due to their capabilities to distinguish executive function problems (Conners, 2000; Doyle, Biederman, Seidman, Weber, & Faraone, 2000). The two newer tests utilized in the present analysis provide a Stroop-like interference measure independent of reading ability (CIT number-count) and an assessment of working memory (digit span tests). To limit the number of initial statistical tests, a principal components analysis (PCA) with varimax rotation was performed on the correlation matrix of these measures (transformed to z-scores), to derive primary components. The primary analysis of interest involved calculating the Pearson r correlation coefficients between media exposure measures and components with eigenvalues >1. To examine relationships to performance on specific tasks, post hoc tests were planned with major contributors to those components significantly related to media exposure measures. The relationship of MEM-PY and DMD scores to interference and intelligence measures was also evaluated via correlations. Additional supplementary analyses were conducted with video-game exposure self-reports from the VRI. SPSS 20 (IBM, Chicago, IL) was employed for all statistical tests. 2.4.1. VBM analysis Gray and white matter morphometric properties were examined with Statistical Parametric Mapping software (SPM8; http:// www.fil.ion.ucl.ac.uk), run via Matlab version 2012a (Mathworks, Natick, MA). The VBM8 toolbox (http://dbm.neuro.uni-jena.de/ vbm/) was utilized to segment each brain into gray matter, white matter, and cerebrospinal fluid (CSF). This segmentation is used to assess the relationship of self-reported television violence exposure to structural properties. A standard VBM protocol was followed (Ashburner & Friston, 2000), using VBM8 default options. First, MRI images were spatially normalized to the ICBM-152 standard anatomical template in Montreal Neurological Institute (MNI) standard space using the Diffeomorphic Anatomical Registration Through Exponential Lie Algebra (DARTEL) approach to improve inter-subject alignment (Ashburner, 2007). An automated procedure then segmented each image into gray matter, white matter, and cerebrospinal fluid (CSF) based on templates for a large adult sample (Good et al., 2001). Modulated data were used to account for individual differences in global brain size. Normalized, modulated data were smoothed with an 8-mm full-width-at-half-maximum (FWHM) Gaussian kernel to reduce noise artifacts. Potential artifacts were identified as those brains with volumes more than two standard deviations

from the mean covariance, and these were carefully visually inspected for poor segmentation. Whole-brain regression analyses for gray and white matter maps were conducted with both overall television viewing and television violence measures as regressors, separately for each method of self-report. Participant age was used as a regressor of no interest to control for biological effects on brain volume that are unrelated to media exposure. Statistics associated with multiple linear regression models employed here do not require predictor variables (only underlying error) to have a normal distribution. An absolute threshold mask of 0.1 was used, meaning only voxels with gray or white matter values >0.1 were used. An individual voxel threshold of p < .005 was used, with a cluster-level familywise error (FWE) p-value correction of p < .05 to correct for multiple comparisons. 3. Results 3.1. Demographics Sixty-five participants successfully completed both sessions of the study, completing daily diary measures and returning for a second visit six to ten days after the first. Most of these participants were unmarried (82%, though 15% were cohabitating with nonspouse romantic partner) and 43% were students (part-/full-time status was not clarified). The sample was comprised of 53 nonHispanic Caucasian (81.5%), two Hispanic (3.1%), four AfricanAmerican (6.2%), five East Indian (7.7%), and one multi-racial individual(s) (1.5%). 3.2. Media exposure measures Mean reported values for total television exposure were nearly identical for the MEM-PY and DMD (Table 2), reflecting between 1 and 2 hours of daily television viewing (though this hourly equivalent may have been higher without an upper bound to the scale). Because Shapiro–Wilk normality tests indicated skewed distributions for media exposure measures (all W < .88, p < .001 except DMD total TV exposure: W = .97, p = .10), nonparametric Spearman rank correlation tests were performed. Total exposure was highly correlated between the indicators (q(63) = .53 p < .001). Injury ratings were higher from the MEM-PY but were significantly correlated with DMD injury ratings (q(63) = .40, p = .001). Because the television violence score is simply a product of these preceding measures, it was likewise correlated between measures (q(63) = .28, p = .02). There was no significant relationship between total television exposure and injury rating from either the MEM-PY (q(63) = .01, p = .92) or DMD (q(63) = .06, p = .64). Due to screening requirements,

Table 2 Sample characteristics and reported media exposure. Mean (SD) Characteristics Age (years) Education (years) IQ Video game play (hours/week) Violent video game play (hours/week) Media exposure MEM-PY total TV time DMD total TV time MEM-PY injury rating DMD injury rating MEM-PY violence score DMD violence score

23.7 14.6 109.9 1.9 0.9 5.3 5.3 1.3 0.6 6.6 3.0

(3.3) (1.7) (12.1) (1.3) (0.7) (1.5) (1.8) (0.7) (0.6) (4.1) (3.1)

30

T.A. Hummer et al. / Brain and Cognition 88 (2014) 26–34

participants reported low amounts of weekly video game play (Table 2). Since movie viewing was sporadic (only 15 individuals reporting seeing a movie during the diary week), individual variability was likely exaggerated and yearly estimates poorly estimated. Movie viewing was therefore not included in analyses. Regardless, MEM-PY violent movie exposure was correlated with MEM-PY television violence scores (q(63) = .30, p = .01), indicating that such exposure is not a distinct factor.

from television violence exposure, as partial correlations separately controlling for MEM-PY (q(61) = .30, p = .02) or DMD (q(61) = .25, p = .047) violence measures revealed similar results. No significant relationship was found between executive function and total video game exposure. Violent video game experience was correlated with the DMD TV violence score (q(63) = .31, p = .01) but not the MEM-PY violence measure (q(65) = .10, p = .42). Total video game exposure was not related to total television viewing as measured by either the MEM-PY or DMD.

3.3. Executive functioning 3.4. Voxel-based morphometry A PCA of executive function measures revealed two primary components (Fig. 1), with interference and CCPT measures loading on an ‘‘Attention–Inhibition’’ factor (k = 2.01) and digit span measures primarily loading on a ‘‘Working Memory’’ component (k = 1.50) (see Supplementary Table 1 for correlations between executive function measures). For both MEM past-year and DMD measures, overall TV exposure was unrelated to either executive function factor and violent TV exposure negatively correlated only with the Attention–Inhibition component (Table 3; Fig. 2). Specifically, greater exposure to violent television was associated with poorer performance on tests of cognitive inhibition, attention and interference control. Post-hoc correlations revealed that both MEM-PY and DMD television violence measures were related to poorer performance on the Conner’s Continuous Performance Test (CCPT), as indicated by both inhibition and attention measures (Table 4). Although SCWT and CIT raw scores were not significantly correlated with media violence exposure, Stroop interference scores were related to television violence scores (with a trend for CIT interference score), likewise indicating poorer executive function with increased violent TV viewing. Neither MEM-PY nor DMD violence scores was related to nonverbal intelligence, and partial correlations controlling for nonverbal IQ yielded nearly identical results (Supplementary Tables 2 and 3). Overall television viewing was not related to any measure of executive function or intelligence. Additional supplementary analyses revealed that, despite the fact that screening requirements limited the sample to minimal video game users, self-reported violent video game exposure was likewise correlated with the Attention–Inhibition factor (q(63) = .32, p = .01). This relationship may be somewhat distinct

Fig. 1. Executive function component loadings. Plot shows loadings from a principal components analysis of z-scored executive function scores with varimax rotation. Only components with eigenvalue >1 are depicted (Working memory and Attention–inhibition).

Neuroimaging data from one participant with sufficient diary data had poor segmentation and was not included in VBM analysis. For the remaining 64 participants, morphometric analysis revealed that white matter volume was negatively associated with DMD TV violence scores. A large cluster (2806 voxels; 9470 mm3), extending from right frontal lobe to parietal cortex demonstrated significantly lower white matter as violence scores increased (Fig. 3). This cluster remains significant if controlling for IQ (2961 voxels) or total intracranial volume (2478 voxels). According to the ICBMDTI-81 white matter atlas (Mori et al., 2008), this cluster includes portions of the superior longitudinal fasciculus and superior corona radiata. Although this cluster shows the most prominent effect and is the only one to survive the FWE cluster-level correction, the relationship may not be limited to this cluster. A significant set-level result (p < .001; FWE-corrected) indicates more clusters of significant voxels (34 clusters) throughout the brain than expected by chance. Residual data extracted from the significant cluster, using the MarsBaR program (http://marsbar.sourceforge.net), confirmed that underlying noise followed a normal distribution (Shapiro–Wilk W = .972, p = .16). There was no association with gray or white matter volume for any other media exposure measures, after correcting for multiple comparisons. 4. Discussion This work extends evidence that violent television exposure is related to poorer performance on certain components of executive function, regardless of overall television viewing habits. Namely, violent television exposure in adult males was associated with increased impairment in inhibition, interference control, and attention, with no such relationship with working memory or nonverbal intelligence. Moreover, violent television exposure was related to reduced white matter volume, most strongly in a cluster ranging from right parietal cortex to the frontal lobe. These results replicate and extend previous work (Kronenberger et al., 2005a), using a sample with different characteristics, an additional method of measuring television violence exposure, and an examination of brain volumetric characteristics. In addition, violent video game play was also related to poor inhibition and attention, even though this sample consisted of minimal users of video games. However, the degree to which this latter relationship is distinct from television habits is unclear (though the correlation remains when controlling for TV violence exposure), as reported violent video game play was positively correlated with diary-measured violent television exposure. In this study, the executive functions negatively associated with violent television viewing were primarily associated with poor inhibition, high impulsivity and lower cognitive control. Lower scores on the SCWT and CIT reflect a poorer ability to inhibit distracting or unnecessary information (MacLeod, 1991), and greater commissions on the CCPT likewise indicate impulsive responding and reduced ability to control inappropriate actions (Conners, 2000; Epstein, Johnson, Varia, & Conners, 2001). Due to

31

T.A. Hummer et al. / Brain and Cognition 88 (2014) 26–34 Table 3 Correlation of television exposure with executive function factors. Factor

Total TV MEM-PY

Attention–Inhibition Working Memory

.06 .03

Injury rating DMD .18 .02

MEM-PY .29* .12

TV violence DMD .33** .10

MEM-PY .25* .16

DMD .27* .05

Spearman rank correlation coefficients for the relationship between television exposure and executive function components are displayed. The negative correlation of Injury Rating and TV violence indicates worse performance on Attention–Inhibition tests with increased violence exposure. * p < .05. ** p < .01.

Fig. 2. Relationship between Attention–Inhibition factor and television violence exposure.

Table 4 Correlation of television exposure with Attention–Inhibition components and nonverbal intelligence. Total TV MEM-PY Stroop color–word CIT score CCPT commissions CCPT reaction time SE Stroop interference CIT interference Nonverbal IQ

.04 .08 .01 .06 .00 .01 .11

Injury rating

TV violence

DMD

MEM-PY

DMD

.17 .20 .13 .05 .05 .11 .02

.16 .33** .09 .28* .18 .12 .04

.21t .16 .36* .39*** .32** .23t .07

MEM-PY .15 .24t .10 .28* .20 .05 .04

DMD .17 .10 .32** .36** .31* .20 .04

Spearman rank correlation coefficients for the relationship between television exposure and major contributors to Attention–Inhibition component, interference scores, and nonverbal intelligence measure are displayed. Higher scores indicated better performance for all tests except the Connors’ Continuous Performance Test (CCPT), for which lower scores indicated better executive functioning. SE = Standard Error. t p < .10. * p < .05. ** p < .01. *** p < .001.

the correlational nature of this study, the direction of these relationships is not clear. One possibility is that difficulties are exacerbated by the psychological experience that accompanies repeated exposure to violence. Inhibitory processes from unnecessary aggressive acts may be hampered by the repeated presentation of violence that is rewarded or, at least, goes unpunished (Blair, 1995; Bushman & Anderson, 2002). Importantly, executive functioning was not related to overall viewing levels, which do not necessarily dictate a weakening of inhibitory processes. The higher CCPT reaction time standard error associated with violent television exposure, which is consistent with worse sustained attention, may be related to inhibition as well. Current theory on attention deficit hyperactivity disorder (ADHD) posits that

inhibitory deficits are a prominent factor in attention problems (Barkley, 1997). Weaknesses in attention may also be related to the quick action and high arousal typically involved in depicting violence on television, although it is unclear whether such high action leads to poorer attention, or if individuals with poor attention are drawn to such depictions. Inhibitory deficits, such as those present in ADHD, are commonly associated with delayed white matter development (Gur et al., 1999; Shaw, Gogtay, & Rapoport, 2010; Silveri et al., 2008), as was seen with higher television violence exposure in this study. Participants in this study did not report having ADHD or significant executive function impairments, but the relationship between white matter volume and cognitive performance on attention-

32

T.A. Hummer et al. / Brain and Cognition 88 (2014) 26–34

Fig. 3. Relationship of white matter volume to violent television exposure. Images depict clusters with significantly reduced white matter volume for higher levels of television violence exposure, as measured with daily media diaries (voxel-level p < .005, only clusters >100 voxels are shown), covarying for age.

and inhibition-related tasks is well-established, with frontoparietal connectivity being particularly vital for executive function skills, such as working memory and attention/inhibition (Burzynska et al., 2011; Dodds, Morein-Zamir, & Robbins, 2011; Matsuo et al., 2009; Tamnes et al., 2010a). Because VBM techniques measure gross volume, the white matter tracts most closely associated with DMD violence scores cannot be specifically identified. However, it appears the largest cluster includes frontoparietal connections, such as the superior longitudinal fasciculus. Future work can employ diffusion tensor imaging (DTI) with tract-based spatial statistics to more precisely identify what tracts are affected (and their direction), along with how microstructural white matter characteristics may be associated with media exposure. Thus, impaired or immature frontoparietal connectivity is likely closely related to poorer attention and inhibitory control found with higher television violence exposure in this investigation, though there are multiple possibilities concerning the direction of these relationships. First, delayed white matter development due to purely biological reasons would result in greater impulsivity and poorer inhibition. Young men with these characteristics may simply be drawn to more violent, often action-intense, programming. On the other hand, watching violent television may slow white matter development, and thus executive dysfunction, due to reduced involvement of executive control networks. Extensive engagement of specific neural regions or networks is known to alter structural characteristics in involved regions (Haier, Karama, Leyba, & Jung, 2009; Ilg et al., 2008; Scholz, Klein, Behrens, & Johansen-Berg, 2009), and disengagement of inhibitory mechanisms during TV violence viewing may slow white matter development. The third possibility is that biological neurodevelopmental delays are worsened by TV violence exposure, and vice versa, resulting in a negative spiral. Gray matter structure was not associated with any media exposure measures in this study, which is counter to previous work showing lower orbitofrontal cortex density with more frequent exposure to media violence (Strenziok et al., 2010). In addition, witnessing real-life violence is associated with reduced premotor (Rocha-Rego et al., 2012) or visual cortex volume (Tomoda et al., 2012). However, these investigations either took place or

measured events that occurred specifically during childhood and adolescence, when the trajectory of gray matter development is less stable relative to young adulthood. Gray matter changes may be more difficult to detect in young adults, compared to white matter, which typically increases in volume and other maturational measures well into adulthood (Ge et al., 2002; Giorgio et al., 2010; Tamnes et al., 2010b). Therefore, the lack of significant associations between any media measures and gray matter volume may be due to the age of our sample or media reporting methods (which focused on adult behaviors), or may truly indicate that white matter maturation is more closely related to media violence exposure. Experimental and longitudinal work has consistently shown that media violence exposure can lead to more aggressive behavior (Anderson et al., 2003; Huesmann et al., 2003; Paik & Comstock, 1994). A similar longitudinal investigation, measuring executive function and brain structure throughout development would be extremely helpful in order to establishing directionality of the relationships found in this study. Since another limitation of this study was the failure to account for childhood media habits, which may differ from adult viewing habits (Huesmann et al., 2003), a longitudinal study could test the relative influence of child and adult media violence exposure on executive functioning and brain structure throughout development. The weaker inhibitory control with increased violent television exposure may be related to more difficulty controlling aggressive thoughts, feelings and behaviors. Aggressive behavior is mediated by a neural circuit that involves striatal and limbic regions, which are regulated in part by the frontal lobe (Davidson, Putnam, & Larson, 2000; Filley et al., 2001). A current model of aggression posits high arousal of regions involved in processing emotion, in particular the amygdala, striatum, and anterior cingulate. This activation, coupled with low activity of frontal regions, results in poor inhibition of aggressive thoughts and feelings, subsequently leading to violent behavior. Neuroimaging of both aggressive youths and those with high media violence exposure has revealed frontal lobe deficits during executive functioning tasks (Mathews et al., 2005), perhaps due to delayed brain maturation. Measurements of media violence were similar between the two methods of acquiring self-reports, and relationships between the measures of executive functioning and estimates of viewing habits for the past year were likewise similar. For this reason, it is unclear why only DMD TV violence scores were related to white matter structure, although both shared a similar relationship with neurocognitive measures. We do not know whether MEM-PY or DMD scales yields a better measure of typical viewing habits. The DMD is likely a more accurate record of television viewing, since participants complete a form at the end of each day, yet only a short period of time (i.e., 1 week) is covered. Therefore, the degree to which DMD scores can act as a proxy for average viewing habits over a longer time period is not totally clear. On the other hand, retrospective media questionnaires such as the MEM-PY are subject to individual differences in selective memory and estimation techniques, which can be subject to reporting biases (Kronenberger et al., 2005b), and may be particularly poor in youth with behavioral problems (Kronenberger et al., 2004). Future research can attempt to hone this method by employing diary techniques over a period of multiple weeks or months to get a better estimation of average media exposure. Of course, as with much psychological research, these methods rely on accurate self-reporting—of video game use, television viewing, and violent content. Accurate measures of media exposure pose a difficulty for researchers, as no single technique is perfect (Vandewater & Lee, 2009). The American Time Use Survey (2008–2011 databases) indicates that males in the 18–29 age range watched an average of 2.5 hours/day of television as a

T.A. Hummer et al. / Brain and Cognition 88 (2014) 26–34

primary activity. Recoding each ATUS response to its equivalent response from the DMD scale (Table 1), which necessarily truncates the highest responses, reveals a mean of 5.1 (versus 5.3 for the current sample). This provides evidence that our sample is similar in terms of total television exposure of the U.S. male population in the same age range, even though our sample was purposefully comprised of young men with lower video game experience. On the other hand, Nielsen surveys indicate that weekly television exposure is between 3–4 hours/day for individuals in the 18– 34 age range (Nielsen, 2013). While devices that directly record television use (e.g., set-top boxes) may provide more precise data, such products have inherent difficulties assessing specifically who is watching or how closely a program is monitored. In addition, because DMD and MEM measures have an upper limit (5 or more hours per day), it is not possible to fully compare this sample’s viewing characteristics to Nielsen data (the distribution of Nielsen data is not clear). In other words, the degree to which this sample represents typical television exposure is not clear. Of course, the sample was purposely designed to be somewhat atypical in terms of video game experience, which may help to assign relationships most closely to television exposure (versus other media) but may limit generalizability. This study provides support for an association between violent television viewing, poorer executive function and lower white matter volume in young adult men, with no relationship to overall television exposure or nonverbal intelligence. The sample in the current study was solely healthy adult males with relatively low video game experience. Even without showing clinical symptoms, men who watched more violent TV performed worse in laboratory tests of executive function and had decreased white matter volume. Additional research is necessary to extend these findings to adult women, particularly given the unclear role of sex differences in the relationship between media violence and aggression (Bartholow & Anderson, 2002). Nonetheless, this evidence adds to emerging research indicating a relationship between media violence exposure and neuropsychological and frontal lobe abnormalities, which complements the extensive research demonstrating that media violence exposure is associated with aggression. The potential interplay of these features highlights the need for continued research to determine the influence of media exposure on social, cognitive and behavioral functioning.

Acknowledgment This research was supported by the Center for Successful Parenting, Carmel, Indiana.

Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.bandc.2014.04. 010. References Anderson, C., Berkowitz, L., Donnerstein, E., Huesmann, L., Johnson, J., Linz, D., et al. (2003). The influence of media violence on youth. Psychological Science in the Public Interest, 4(3), 81–110. Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38(1), 95–113. Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry – The methods. NeuroImage, 11(6), 805–821. Barkley, R. A. (1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin, 121(1), 65–94.

33

Bartholow, B. D., & Anderson, C. A. (2002). Effects of violent video games on aggressive behavior: Potential sex differences. Journal of Experimental Social Psychology, 38(3), 283–290. Bartholow, B. D., Bushman, B. J., & Sestir, M. A. (2006). Chronic violent video game exposure and desensitization to violence: Behavioral and event-related brain potential data. Journal of Experimental Social Psychology, 42(4), 532–539. Blair, R. J. (1995). A cognitive developmental approach to mortality: Investigating the psychopath. Cognition, 57(1), 1–29. Brickman, A. M., Zimmerman, M. E., Paul, R. H., Grieve, S. M., Tate, D. F., Cohen, R. A., et al. (2006). Regional white matter and neuropsychological functioning across the adult lifespan. Biological Psychiatry, 60(5), 444–453. Brower, M. C., & Price, B. H. (2001). Neuropsychiatry of frontal lobe dysfunction in violent and criminal behaviour: A critical review. Journal of Neurology, Neurosurgery and Psychiatry, 71(6), 720–726. Burzynska, A. Z., Nagel, I. E., Preuschhof, C., Li, S. C., Lindenberger, U., Backman, L., et al. (2011). Microstructure of frontoparietal connections predicts cortical responsivity and working memory performance. Cerebral Cortex. Bushman, B. J., & Anderson, C. A. (2002). Violent video games and hostile expectations: A test of the general aggression model. Personality and Social Psychology Bulletin, 28(12), 1679. Bushman, B. J., & Geen, R. G. (1990). Role of cognitive-emotional mediators and individual differences in the effects of media violence on aggression. Journal of Personality and Social Psychology, 58(1), 156–163. Choi, J., Jeong, B., Polcari, A., Rohan, M. L., & Teicher, M. H. (2012). Reduced fractional anisotropy in the visual limbic pathway of young adults witnessing domestic violence in childhood. NeuroImage, 59(2), 1071–1079. Cohen, M. J. (1997). Children’s memory scale. San Antonio, TX: Psychological Corporation. Conners, C. K. (2000). Conners’ CPT II technical guide and software manual. North Tonawanda, NY: Multi-Health Systems. Cooley, M. R., Turner, S. M., & Beidel, D. C. (1995). Assessing community violence: The children’s report of exposure to violence. Journal of the American Academy of Child and Adolescent Psychiatry, 34(2), 201–208. Davidson, R. J., Putnam, K. M., & Larson, C. L. (2000). Dysfunction in the neural circuitry of emotion regulation – A possible prelude to violence. Science, 289(5479), 591–594. Dodds, C. M., Morein-Zamir, S., & Robbins, T. W. (2011). Dissociating inhibition, attention, and response control in the frontoparietal network using functional magnetic resonance imaging. Cerebral Cortex, 21(5), 1155–1165. Doyle, A. E., Biederman, J., Seidman, L. J., Weber, W., & Faraone, S. V. (2000). Diagnostic efficiency of neuropsychological test scores for discriminating boys with and without attention deficit-hyperactivity disorder. Journal of Consulting and Clinical Psychology, 68(3), 477–488. Epstein, J. N., Johnson, D. E., Varia, I. M., & Conners, C. K. (2001). Neuropsychological assessment of response inhibition in adults with ADHD. Journal of Clinical and Experimental Neuropsychology, 23(3), 362–371. Eron, L. D., Huesmann, L. R., Lefkowitz, M. M., & Walder, L. O. (1972). Does television violence cause aggression? American Psychologist, 27(4), 253–263. Filley, C. M., Price, B. H., Nell, V., Antoinette, T., Morgan, A. S., Bresnahan, J. F., et al. (2001). Toward an understanding of violence: Neurobehavioral aspects of unwarranted physical aggression: Aspen Neurobehavioral Conference consensus statement. Neuropsychiatry Neuropsychology and Behavioral Neurology, 14(1), 1–14. Foster, E. M., & Watkins, S. (2010). The value of reanalysis: TV viewing and attention problems. Child Development, 81(1), 368–375. Gadow, K. D., Sprafkin, J., & Weiss, M. D. (2004). Adult symptom inventory-4 manual. Stony Brook, NY: Checkmate Plus. Ge, Y., Grossman, R. I., Babb, J. S., Rabin, M. L., Mannon, L. J., & Kolson, D. L. (2002). Age-related total gray matter and white matter changes in normal adult brain. Part II: Quantitative magnetization transfer ratio histogram analysis. AJNR. American Journal of Neuroradiology, 23(8), 1334–1341. Giancola, P. R. (1995). Evidence for dorsolateral and orbital prefrontal cortical involvement in the expression of aggressive behavior. Aggressive Behavior, 21(6), 431–450. Giorgio, A., Santelli, L., Tomassini, V., Bosnell, R., Smith, S., De Stefano, N., et al. (2010). Age-related changes in grey and white matter structure throughout adulthood. NeuroImage, 51(3), 943–951. Golden, C. J. (1978). Stroop color and word test. Chicago, IL: Stoelting. Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14(1 Pt 1), 21–36. Gur, R. C., Turetsky, B. I., Matsui, M., Yan, M., Bilker, W., Hughett, P., et al. (1999). Sex differences in brain gray and white matter in healthy young adults: Correlations with cognitive performance. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 19(10), 4065–4072. Haier, R. J., Karama, S., Leyba, L., & Jung, R. E. (2009). MRI assessment of cortical thickness and functional activity changes in adolescent girls following three months of practice on a visual–spatial task. BMC Research Notes, 2, 174. Huesmann, L. R., Moise-Titus, J., Podolski, C.-L., & Eron, L. D. (2003). Longitudinal relations between children’s exposure to TV violence and their aggressive and violent behavior in young adulthood: 1977–1992. Developmental Psychology, 39(2), 201–221. Hummer, T. A., Kronenberger, W. G., Wang, Y., Dunn, D. W., Mosier, K. M., Kalnin, A. J., et al. (2011). Executive functioning characteristics associated with ADHD comorbidity in adolescents with disruptive behavior disorders. Journal of Abnormal Child Psychology, 39(1), 11–19.

34

T.A. Hummer et al. / Brain and Cognition 88 (2014) 26–34

Hummer, T. A., Wang, Y., Kronenberger, W. G., Mosier, K. M., Kalnin, A. J., Dunn, D. W., et al. (2010). Short-term violent video game play by adolescents alters prefrontal activity during cognitive inhibition. Media Psychology, 13(2), 136–154. Ilg, R., Wohlschlager, A. M., Gaser, C., Liebau, Y., Dauner, R., Woller, A., et al. (2008). Gray matter increase induced by practice correlates with task-specific activation: A combined functional and morphometric magnetic resonance imaging study. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 28(16), 4210–4215. Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman brief intelligence test (2nd ed.). Circle Pines, MN: American Guidance Service. Kronenberger, W. G., Mathews, V. P., Dunn, D. W., Wang, Y., Wood, E. A., Giaque, A. L., et al. (2004). Adolescent aggressive behavior and parent awareness of media violence exposure. Paper presented at the Convention of the American Psychological Association, Honolulu, HI. Kronenberger, W. G., Mathews, V. P., Dunn, D. W., Wang, Y., Wood, E. A., Giaque, A. L., et al. (2005a). Media violence exposure and executive functioning in aggressive and control adolescents. Journal of Clinical Psychology, 61(6), 725–737. Kronenberger, W. G., Mathews, V. P., Dunn, D. W., Wang, Y., Wood, E. A., Larsen, J. J., et al. (2005b). Media violence exposure in aggressive and control adolescents: Differences in self-and parent-reported exposure to violence on television and in video games. Aggressive Behavior, 31(3), 201–216. Landhuis, C. E., Poulton, R., Welch, D., & Hancox, R. J. (2007). Does childhood television viewing lead to attention problems in adolescence? Results from a prospective longitudinal study. Pediatrics, 120(3), 532–537. Lefkowitz, M. M., Eron, L. D., Walder, L. O., & Huesmann, L. R. (1977). Growing up to be violent: A longitudinal study of the development of aggression. New York: Pergamon Press. Mabbott, D. J., Noseworthy, M., Bouffet, E., Laughlin, S., & Rockel, C. (2006). White matter growth as a mechanism of cognitive development in children. NeuroImage, 33(3), 936–946. MacLeod, C. M. (1991). Half a century of research on the Stroop effect: An integrative review. Psychological Bulletin, 109(2), 163–203. Mathews, V., Kronenberger, W., Wang, Y., Lurito, J., Lowe, M., & Dunn, D. (2005). Media violence exposure and frontal lobe activation measured by functional magnetic resonance imaging in aggressive and nonaggressive adolescents. Journal of Computer Assisted Tomography, 29(3), 287. Matsuo, K., Nicoletti, M., Nemoto, K., Hatch, J. P., Peluso, M. A., Nery, F. G., et al. (2009). A voxel-based morphometry study of frontal gray matter correlates of impulsivity. Human Brain Mapping, 30(4), 1188–1195. Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., et al. (2008). Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage, 40(2), 570–582. Newman, L. M., Trivedi, M. A., Bendlin, B. B., Ries, M. L., & Johnson, S. C. (2007). The relationship between gray matter morphometry and neuropsychological

performance in a large sample of cognitively healthy adults. Brain Imaging and Behavior, 1(1–2), 3–10. Nielsen (2013). The cross-platform report: Quarter 2, 2013-US. The Nielsen Company. Olson, E. A., Collins, P. F., Hooper, C. J., Muetzel, R., Lim, K. O., & Luciana, M. (2009). White matter integrity predicts delay discounting behavior in 9- to 23-yearolds: A diffusion tensor imaging study. Journal of Cognitive Neuroscience, 21(7), 1406–1421. Paik, H., & Comstock, G. (1994). The effects of television violence on antisocialbehavior – A metaanalysis. Communication Research, 21(4), 516–546. Rocha-Rego, V., Pereira, M. G., Oliveira, L., Mendlowicz, M. V., Fiszman, A., MarquesPortella, C., et al. (2012). Decreased premotor cortex volume in victims of urban violence with posttraumatic stress disorder. PLoS One, 7(8), e42560. Scholz, J., Klein, M. C., Behrens, T. E., & Johansen-Berg, H. (2009). Training induces changes in white-matter architecture. Nature Neuroscience, 12(11), 1370–1371. Shaw, P., Gogtay, N., & Rapoport, J. (2010). Childhood psychiatric disorders as anomalies in neurodevelopmental trajectories. Human Brain Mapping, 31(6), 917–925. Silveri, M. M., Tzilos, G. K., & Yurgelun-Todd, D. A. (2008). Relationship between white matter volume and cognitive performance during adolescence: Effects of age, sex and risk for drug use. Addiction, 103(9), 1509–1520. Strenziok, M., Krueger, F., Pulaski, S. J., Openshaw, A. E., Zamboni, G., van der Meer, E., et al. (2010). Lower lateral orbitofrontal cortex density associated with more frequent exposure to television and movie violence in male adolescents. Journal of Adolescent Health, 46(6), 607–609. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662. Tamnes, C. K., Ostby, Y., Fjell, A. M., Westlye, L. T., Due-Tonnessen, P., & Walhovd, K. B. (2010a). Brain maturation in adolescence and young adulthood: Regional age-related changes in cortical thickness and white matter volume and microstructure. Cerebral Cortex, 20(3), 534–548. Tamnes, C. K., Ostby, Y., Walhovd, K. B., Westlye, L. T., Due-Tonnessen, P., & Fjell, A. M. (2010b). Intellectual abilities and white matter microstructure in development: A diffusion tensor imaging study. Human Brain Mapping, 31(10), 1609–1625. Tomoda, A., Polcari, A., Anderson, C. M., & Teicher, M. H. (2012). Reduced visual cortex gray matter volume and thickness in young adults who witnessed domestic violence during childhood. PLoS One, 7(12), e52528. Vandewater, E. A., & Lee, S. J. (2009). Measuring children’s media use in the digital age: Issues and challenges. The American Behavioral Scientist, 52(8), 1152–1176. Wang, Y., Mathews, V. P., Kalnin, A. J., Mosier, K. M., Dunn, D. W., Saykin, A. J., et al. (2009). Short term exposure to a violent video game induces changes in frontolimbic circuitry in adolescents. Brain Imaging and Behavior. http:// dx.doi.org/10.1007/s11682-008-9058-8. Zimmerman, F. J., & Christakis, D. A. (2007). Associations between content types of early media exposure and subsequent attentional problems. Pediatrics, 120(5), 986–992.